The visibility of outdoor images is often degraded by turbid medium in poor weather such as haze, fog, sandstorms, and so on. Optically, poor visibility in digital images is due to the substantial presence of different atmospheric particles which absorb and scatter light between a digital camera and a captured object. Image degradation may cause problems for many systems which must operate under a wide range of weather conditions such as outdoor object recognition systems, obstacle detection systems, video surveillance systems, and intelligent transportation systems.
In order to improve visibility in hazy images, haze removal techniques have been recently proposed. These may be divided into three major categories: an additional information approach, a multiple-image restoration approach, and a single-image restoration approach.
The additional information approach employs scene information to remove haze and recover vivid colors. Nevertheless, the scene depth information must be provided through user interaction, yet it is scarcely given for an arbitrary image. Thus, such approach does not hold for realistic application in arbitrary images.
The multiple-image restoration approach employs two or more images to estimate scene depth and subsequently remove haze formation. However, such approach mainly requires either a complex computation or a use of additional hardware devices. This may lead to costly restoration expenses. Hence, recent research has been focusing on the single-image restoration approach which is based on strong assumptions or priors.
A prior art method proposes a single-image restoration technique that removes haze by maximizing the local contrast of recovered scene radiance based on an observation that captured hazy images have lower contrast than restored images. However, such technique may result in unwanted feature artifact effects along depth edges. Another prior art method proposes another single-image restoration technique that estimates the albedo of the scene and deduces the transmission map based on an assumption that the transmission shading and the surface shading are locally uncorrelated. However, such technique may not contend with images featuring dense fog.
Yet another prior art proposes a dark channel prior method which uses a key assumption that most local patches for outdoor haze-free images exhibit very low intensity in at least one of color channel, which can be used to directly estimate haze density and recover vivid colors. Until now, such approach has attracted the most attention due to its ability to effectively remove haze formation. Nevertheless, the efficacy of haze removal may change in response to varied weather conditions and scene objects in realistic environments. In particular, such approach may not adequately deal with color distortions and complex structures. In these situations, color shift and artifact effects may occur in restored images.